Tian Wei: Good morning, ladies and gentlemen, thank you so much for joining this session, “Keeping Up with Smart Factories”. Well, from the name itself, one can already tell, the fascinating discussion we are going to have. The Lighthouse Factory initiative coming from the World Economic Forum, as you know, is so well-known over the years. Meanwhile, AI generation technology, in a way, has been putting much more momentum to the overall process.

 

Let us go more detail into how companies are working with it, and what kind of impact will that create. What does it take for this transformation?

 

My name is Tian Wei, I am a host from CGTN, it is a pleasure to be the moderator of this panel.

 

I am more than happy – and honoured – to introduce the great panellists for this discussion: Mr Gan Kim Yong, Deputy Prime Minister from Singapore; Mr Roland Busch, President and CEO of Siemens; Mr Padraig McDonnell, Chief Executive Officer of Agilent;  and also, Stephanie Pullings Hart, Executive Vice President and the Global Head of Operations of Nestle; and last on this list, but not least, is Anish Shah, Group CEO and Managing Director of Mahindra Group. What a pleasure. Thank you so much for joining us.

 

Now, let’s go directly into the conversation. Time is very limited for such a grand topic.

 

So, policy. That is really, extremely important. Mr Deputy Prime Minister, Singapore has been playing a key role in the region and beyond over the past few years in terms of smart manufacturing. Tell us more about what are some of the best experiences that you have acquired, and yet, do you think there are several areas that can still be improved, not only with your regional partners but also with your international partners?

 

DPM Gan: Thank you for inviting me to this dialogue. It is an interesting and exciting topic to talk about smart factories and how do we move forward, particularly with our manufacturing sector. Manufacturing has been a key component of our economy in Singapore, and we partner many of the major MNCs as well as the local companies to grow the manufacturing sector. But the manufacturing sector has to continue to transform itself, to make sure that it continues to be in touch with the latest technology, but at the same time, continue to improve productivity and value-add. Smart factories play a very important role in this aspect.

 

And while policies are important, it is also very important to ensure that the policies are aligned with the interests of the companies and the businesses. Therefore, one of the key drivers of smart factories and adoption of AI and automation is, really, to be driven by the companies themselves. So policies provide support and the necessary measures to allow that to happen, but the companies and the businesses must take the lead in the transformation of the manufacturing facilities.

 

In Singapore, we do it in two ways. First, we work with individual companies, big and small, to help them, enable them, support them, provide incentives for them, to invest in automation and invest in transformation, invest in AI. But we also recognise that many of the smaller companies, the MSMEs, do not have enough resources to be able to tap into some of these latest technologies and automation and AI capabilities. Therefore, we also work with sectoral leaders to set up sectoral-based centres of excellence for manufacturing, so that these smaller companies will be able to tap on these larger facilities, on a shared basis, so that they can also have access to the latest technologies in automation and AI. This way, we allow the entire manufacturing, big or small, to be able to embark on this transformation journey. The bigger ones will be able to do it themselves, within themselves, to set up centres of excellence for manufacturing, automation, AI. The smaller ones will be able to tap on the sectoral-based, broad-based shared facilities for automation and AI, and these are the sectoral centres of excellence.

 

Manufacturing, for example, we just announced an AI centre of excellence for manufacturing. So we want to encourage our manufacturers, the smaller ones particularly, to tap on these resources so that they can embark on this AI transformation journey. Once they are more familiar, they are able to develop their own capabilities, they are free to go on their own and set up their own company-based centre of excellence. This is how, through policy, we help to support both large companies as well as small companies in manufacturing to embark on this journey.

 

Tian Wei: Thank you, Mr Deputy Prime Minister. Not only the content of your answer, but also the speed of your speaking, seems to give a lot of consideration to your other panellists, so that they will also have sufficient time.

 

DPM Gan: That’s right, because you keep reminding me that time is short, so I have to say what I can in as short as possible a time.

 

Tian Wei: This is the role of the policy maker, to serve, to provide service. Let’s go to Mr Busch. We have heard from the policy maker. But what about our business players, especially on the ground. On the floor of our factories, these frontier technologies are really translating into real smart manufacturing. Mr Busch.

 

Roland Busch: The question is, what do we want to solve. And what we want to solve is the big challenges, which is not only climate change, scarce resources, but it is also labour shortage, shortage of skilled labour. We want to have localisation, smaller factories, smaller lot sizes, shorter cycle times. That’s the idea. And there is a lot of technologies behind serving these frontier technologies.

 

To frame, it, I will just define it from the example of the industrial intervals. What it is, is a futuristic, physics-based, real-time representation of the real world. Imagine that you have a plant where everything, what you see there, you have in the digital world, including the people that you see working on the shop floor. Real-time, futuristic. And what we need, you definitely need a physics-based digital twin, which behaves like the real world. So an example is, in the gaming world when you move a robot very fast, it is perfect. In the real world, it starts swinging. So you need physics-based, otherwise you cannot really make this real.

 

Second, you need all the hardware to be connected. Next element, it is data and AI. AI is supercharging what we do on the shop floor, how we build this technology stack. And this enables completely new ways of how you really transform manufacturing on your shop floor.  

Last element, and you said it, partners. Even though Siemens has spent six and a half billion on R&D, and we have a strong portfolio, it is not good enough. We need strong partners.

 

And to give some examples, of how it looks like. We have a couple of WEF lighthouse factories, where we are pushing the limits toward industrial intervals. You see a robot alley, you see a lot of, any kind of, automation devices. Whatever we do, we have a digital twin of some cells – not the whole plant yet, but we will get there. What are the benefits? 70% higher productivity, 40% less energy costs, and what’s very important, 40% less time to market. So it’s really amazing how we speed up. Partner? Nvidia. Because if you want to do something futuristic, you better get a strong partner with you.

 

Second example, industrial copilots and related agents. So we are using Microsoft large language models, in that case, to build an industrial copilot in the programming phase. I think many people do that, but also on the shop floor. So that means, it is basically an expert system which interests all the learnings which you have in the past, helping you – if there is a red light, the line stops. And even if you are in the night shift, if you don’t have an expert, you have a copilot. You can ask something, and it comes back and tells you “This problem, we have had a couple of times”, this is the problem, how you solve it, and what to do. Here, again, Microsoft is a partner.

 

The last example, is low code. We want to democratise the way you use software.  So you need low code capabilities, and we have a platform, and we empowering and supercharging this platform.

 

Let me give you one last point. We do that for big customers, when you go with big accounts for the automotive industry, but since you talked about it, how can you scale it. Don’t forget the small and medium companies. There are a couple of 100,000 in China and India, there are 50,000, 100,000 in the United States, 40,000 in Italy. To democratise this technology, to scale it, you really need to make it easy to use, plug and play, and eventually sell it in marketplaces, where you need, again, a strong ecosystem to do that.

 

Tian Wei: I see the light in your eyes when you describe these players. Very exciting, obviously. When you were talking, Mr McDonnell seemed to be always nodding his head because he has similar experiences, talking about lighthouse factories.

 

Padraig McDonnell: Yes, we were just recipients of lighthouse factories – one in Shanghai and one in Penang, and a lot of overlap with Siemens in all the areas. One of the areas, large language models about how we can reduce the number of failures and see where the red lines are, particularly in Penang.

 

And one of the things that we're seeing as well, with AI as well, is how we can train the workforce and make sure that we're developing the workforce with these new skills. Because the workforce is going to have to learn and develop, and of course, there's new skills needed. So having real skills matrixes, understanding what our skills are today and what do they need to be for the future, is one of our big areas. And of course, with Singapore, we have a world-class factory there where we're working with digital twins and working with talent development with the Singapore government, about how we can continue to bring talent in.

 

Last example, in our lighthouse in Shanghai, where we produce our gas chromatographs, which are cornerstone of the analytical lab. We can have 1000 customisations on these systems. You can imagine the complexity with that. AI has helped us to reduce the time from eight days to two days in designing the systems and getting them up and running, doubling the output. And I think it also creates a really learning opportunity in the factory as well.

 

Tian Wei: Well, you are coming from similar areas in terms of sector, but there's one consumer product, from Nestle. Ms Hart, would you like to share with us as to how it works in your sector?

 

Stephanie Pullings Hart: No, I absolutely would love to. And super, super honoured to be a part of this panel. And when we look at where Nestle, or how Nestle performs in the fast moving consumer goods segment. You know, we're in 188 countries. We have 340 manufacturing facilities, 780 distribution facilities around the world. And you know, 270,000 total Nestle employees, but 150,000-ish that are in that operations capacity. So when we look at the unlocking capabilities from an artificial intelligence perspective, or machine learning and digital tools, the possibilities are endless.

 

But, really, building on my colleague’s point here is, you know, that this digital transformation requires an investment in the capabilities for our workforce. We are on this very, very robust journey, and have great partners like Siemens, Aviva that we're working with as well. But one of the things that we were talking about in the green room, when you do have a large number of employees, or even if you have a small number of employees, oftentimes we underestimate the impact of change management. And it is one of the most critical aspects that we are focused on as an organisation, and that I encourage other people that are on the journey to also be focused on, because it could actually pause and really hold back the improvements that you could potentially see.

 

The unlock for us as an organisation, obviously, you know, labour productivity is a no brainer, but it's more around looking at predictive analytics and allowing this generation of employees that we have within the workforce. Because if you think about it, we have people within our workforce that are extremely savvy, so the newer generation that kind of came out of the womb doing this and knows how to do that, and a very mature segment that can be a little uncomfortable and it can be a little a little intimidating.

 

So we've actually done a lot, particularly in our markets in South America, in bridging that gap and pairing new generation with a more mature generation together to unlock the potential and capability. So I'm excited to see what this means for the consumer sector, but it requires the work of partners, of government and of learning institutions.

 

Tian Wei: That's a whole set of fascinating questions as to how to bring everybody on board, not only within the company, but also in the market. Mr. Shah, I know you also have fascinating examples to share with us. Would you like to do that right now?

 

Anish Shah: Let me start with the world around us, which has a very high level of uncertainty, especially the auto industry. We went through uncertainty in supply chains where semiconductors were not available, and we kept getting these messages from our suppliers, that we can't give you the keys for your car, or we can't give you the wireless charging part for your car, or some other part is not available. And we can't shut the factory down because one part is not available. We also have the complexity embedded in our plants, where we got one plant with 22 production shops and 250 variants being made on a real-time basis.

 

And that's where technology has come in, in a big way. AI has helped us simplify a lot of these, and I'm going to outline five areas of AI that we've actually used.

 

One is what I call agility.ai which is essentially the digital twin that Roland mentioned, that helps us reroute production based on something that has gone wrong or based on something that has gone right. If there's greater demand for a particular variant, how can we create more of that? And how can we solve for challenges that we face from a supply chain standpoint? Eventually, this is also going to help us do real-time production, where a consumer can order a particular vehicle with a certain set of features, a certain colour, and we can show them on the production line, this is where you are, and then this is how your vehicle has been produced.

 

The second one I call quality.ai which is helping us on a number of aspects of quality. One is around weld integrity. Now, weld integrity typically had to be done by sort of destructing something, and to check whether that is done well or not. There are a lot of parameters that go into understanding what a good weld is, and those parameters have been put into a model. We can use that model today, to look at it and say we don't need to go back and destruct something or check for quality. We know right up front whether this is a good weld or not, and we can correct for it immediately. Linked to that also is paint shops, and we use our vision.ai capabilities to look at paint on a vehicle and make sure that it's the right quality, there are no spots that are left out, and there's no rework that's required. And that's that also results in less use of paint, which saves us cost as well.

 

The next one I look at is uptime.ai. And here, in the past, we've had our history in the minds of people, recorded, in a sense, on some platforms, and people look at it. If a machine's down, what do I do? Now we actually have a bot that or an agent that walks through, here's what you should do, and the bot has information fed and from machine manuals as well as our history, to be able to reduce downtime. And we will reduce downtime by about 10 to 15% depending on which shop you're operating in, using this.

 

The fourth one I'd look at is energy.ai and how do we optimise energy in our plants, and there are again, significant savings that we've gotten from doing this. We're not complete on this as yet. This is still a little bit of work in progress.

 

And the last one is connected.ai, which is, how do we connect all our machines together? Again, Roland, something that you mentioned, something a little more straightforward than some of the other ones, but the net from all of this is, what are the outcomes? And does this allow us to look at investments in each space? What do we get? But more importantly, in the marketplace, how can we compete better? And we've just launched two electric vehicles, and the questions we're getting back from customers, as well as our investors, is, how can you afford to do this at such a low price, as compared to the quality you're offering for these vehicles? Are you making losses? I just had a media interview this morning when I was being grilled on this, is this an introductory price? And I said, “No, we actually have good margins on this”. We just have better manufacturing tools that allow us to do it, so the outcomes are more important from that perspective.

 

Tian Wei: You have very considerate customers.

 

DPM Gan: Can I ask a question? Would it be possible to see how we can allow your experience to be shared by other industries, because you've done very well, and have the five AI modules, and it will be very useful, I think, for the world. And if you can share with some of the industry, have a platform to allow some of your knowledge and your application and your approach to be shared by other companies. Maybe Nestle can learn something from how you apply AI or, Siemens.

 

Tian Wei: In other words, cross-sector.

 

Stephanie Pullings Hart: Yes, and that's actually what I was going to add. Just as Mr Shah was actually speaking, the aspects that he's highlighted around connectivity, around improvements, from a sustainability, the whole aspect is exactly the areas that we're focused on, that we're actually doing. We use different terminology.

 

But what I was going to highlight is it doesn't even matter which sector that we're in, automotive, food and consumer goods, the benefits and the opportunities that exist are really the same. And it's driving consistent quality, it's actually diving into data. And I think data, or accurate data, is the currency of the future. And how you understand, and the individuals that you actually bring into the organisation that know how to handle and interpret data and AI, are the ones that are actually going to be leading us to the future.

 

Tian Wei: Stephanie, it is very much a human-centered. Very much how it works. Let me have Mr Busch, and then I go to Mr McDonnell.

 

Anish Shah: Please, let me just respond to this question, which is, we are actually doing this today because a tech-minded business, is actually bringing a lot of its customers to our plant for them to see what's happening there. And part of what we are also struggling with, which I'll be candid on, is that the fact that this is a competitive edge for us, because we feel that we can make products 30% cheaper than auto manufacturers around the world.

 

Roland Busch: At this point, let me come in because this is how we make our living. We are not OEMs. I mean, we are Tier One. We have one exception. We build trains. This is one exception. And there's a very good one, which we basically are scaling technology. We are scaling technology, all the way from the machine builders, machine tooling, which you have all in your sites. And then we bring technology to, I mean, all verticals, food and beverage, pharmaceutical, chemical, oil and gas, defense, aerospace, automotive. I mean, all these are our customers. And this is the beauty about it, what I talked about before, platform paste technologies that you have certain elements which scale across.

 

So the machine learning algorithm, which allows us to predict with a lead time of five days to 10 days, which door of a train is going to fail and why. So we put it aside, repair it and achieve almost 100% availability. The very same machine learning algorithm runs on a welding machine and detects a wrong welding spot, the technology which allows us, for example, to optimise new machines - when you fill a yogurt and you change your mixture, you have a lower fluidity and you don't want to spill. So we train it rather than you stop in line, we know exactly, and simulating how it goes, so that you can just change your recipe and run it.

 

The same algorithm runs in a chemical plant where you have batch processing. So, this is the beauty about technology. And maybe one thing to add, about the people. And this is so important. The job description of our blue-collared labour today is completely unlike the job description we have tomorrow. So that means when we are training with our industrial co-pilot, we want to democratise technology, to make it accessible via natural language, so people can programme a robot as natural language. It should be an agent. That means, you tell and it does what you tell them to do. It needs a certain hardened technology, but then it really scales if you do it right, and you have the right people to use it on the shopfloor.

 

Padraig McDonnell: Yeah, I think you know, what you really have to remember is the why in all of this. We're increasing productivity, we're making it easier for workers to, of course, create  new products. But it's really bringing us closer to the customer. It brings innovation closer to the customer, and it actually allows us to interact closer with the customer. And beforehand, you know, sustainability and productivity were seen as maybe different things. AI is allowing those to merge in terms of our sustainability goals and productivity, helping with that in terms of costs, etc. And when you think about the purpose of a company and the people in the company, aligning that purpose to the customer outcomes and using technology to bring these outcomes quicker, I think, is one of the key areas from a change management point of view.

 

Tian Wei: I see everybody is having a great time on this panel. Either we have the topic right, or we just have the most fascinating panelists, or both.

 

But now I'm going to ask harder questions, after having fun. So we are excited about this trend. While we are excited about it, there are a lot of questions that are still unanswered, even though we have already gradually found some answers.

 

What about the relationship between human and AI-generated technologies, or robots, or, the list goes on. Have you given some more sophisticated thinking about this link? Meanwhile, two to five years, technology is going to soar. Of course, with a lot of uncertainties, which Mr Busch kindly reminded us of, at the very beginning of his answer.

 

But how do you see these technologies are really going? Especially, how would they intertwine during the process? I think I want to know more about that. Having said that, though, and how will policy, you know, accommodate? Well, all these factors are evolving. So, Mr Deputy Prime Minister, I give you, once again, a first opportunity, you can take your time to answer.

 

DPM Gan: Thank you so much. I think this is very a very important question. In fact,it  has been a question that Singapore Government – and I'm sure governments around the world – have been focusing on. How do we ensure the journey towards automation and AI will be beneficial, not to just a small segment of companies and workers, but broad-based, so that everyone can see the benefit of it.

 

This is a very similar experience to what we have gone through in the early days, when we have gone through the basic automation revolution. I think it will also impact on the workforce, workers and businesses. Some businesses have to transform and therefore, this transition is very important, and how well we manage the transition will also determine how successful we will be at the end of the journey when we emerge from transformation.

 

So I think from the Singapore Government’s point of view, we pay a lot of attention in managing the workforce transformation. And we develop Industry Transformation Maps for all the key sectors of industries in Singapore. And on top of that, within each Industry Transformation Map, we also have developed manpower transformation strategies for each of the industry sectors, to make sure that we are well prepared for the future as industry goes through a transformation journey. 

 

And one of the key focus areas in the manpower transformation map is focusing on training and retraining and reskilling. And we introduced a scheme called SkillsFuture Singapore, to allow Singapore workers to tap on the resources the Government provides, in terms of subsidies for the cost, fee of training, even in terms of subsidies for their payroll, because some of them may have to take no=pay leave for long period of time to go through formal, long-form of education, to take another diploma or another degree, so that they are able to change their career pathways to something that they've never learned before.

 

These trainings are very important and they are costly, but the Government is prepared to invest in them. So we provide subsidies for the training courses. At the same time, we provide support for their income, if they have to leave their job, or take no-pay leave in order to pursue this training. But we think it is very important part of our journey towards this transformation. And therefore, I think we are emphasising on this.

 

But it's also important for us to partner the industries very closely, an earlier point I had mentioned in the beginning. That this transformation, these training courses and upgrading must be relevant to the industries, and we are very keen to partner our industries to embark on this training journey.  And therefore, we also introduce a concept called company training committees. We support and subsidise companies to set up company training committees within the company to decide what kind of a training do you need, so that the training we provide is relevant to the company's needs. We also know that some companies are not keen, because they feel that once the workers are trained, they may leave the company and do something else. So we also provide independent training and support for workers who want to change their pathways. So, I think training and transition management and involvement, engagement or partnership with the company is very important.

 

Tian Wei: Well, we all know Singapore is one of the most advanced economies in the world. You have got a lot of cash, sir.

 

DPM Gan: It is never enough.

 

Tian Wei: Yeah, it is never enough, that’s for sure. Well, sitting here, we have a diversity of economies coming from all over the world. So what you're talking about, are they applicable elsewhere? Have you ever thought about that question as a policy maker?

 

DPM Gan: Different countries will have different economies, with different considerations, different priorities. And many countries actually have a lot of resources, and sometimes it’s not just about money, but about the concept, about the determination, about the focus on what you want to do. And this, in a way, if you look at it, it's an investment in the future, it is not a cost that you write off. So it's something that's important for companies around the world, governments around the world, to pay attention to. Training and upgrading, so that you are able to transform the workforce to be in line with the development and evolution of the industries.

 

Tian Wei: And they have to do it fast.

 

DPM Gan: They have to do it fast.

 

Tian Wei: Mr Deputy Prime Minister, talking about how he has a lot of transformational maps. What about for you, Mr McDonnell? Are you also having a lot of maps?

 

Padraig McDonnell: Yes, lots of maps. And I think, you know, frontier technologies, when you think about it, it really is about accelerating and enhancing human potential, the ability to learn going forward for two reasons. It's not only to increase productivity, but also to enhance innovation. And in one of our plans, when we're using augmented reality with our workers, and when we're using advanced models, we actually see the attrition level drop to one-third of what we see in the marketplace, because we're giving people opportunities to learn new skills, really allows us to bring out more complex products faster. And you know, it's going to really also help hyper customisation that's going to be required by our customers. So I really think about it as an integrator for enhancing human potential.

 

Tian Wei: What about that human machine collaboration, what does that look like? You know, not just for now, which you all described. What about two to five years, Mr Busch?

 

Roland Busch: I think the first thing is also in a broader context, to have to take the anxiety away from the people, that this costs my job. I cannot learn it. I mean, the young generation is easy to adapt, but the more mature ones, they need to learn this technology. So I think people have to get used to having a colleague, a digital colleague, a digital agent. I mean, you have a lot of colleagues and have a new one, a digital one. And these guys are growing, and they are smart, but they will never, they will never take your job away. They take your existing job away, but it creates a new one, which makes a difference.

 

And coming to your point, education and training, how you do that, how you prompt. And taking the people along with you. We are also defining, I think, 30 different job profiles of where you are, what capabilities you have and what you want to learn. Could be a one week training course, could be three or six months. And that pattern of 80,000 people investing more than 400 million euros, in education and training of people.

 

As you go along, and as you start appreciating that your work is just getting much easier and you can dedicate your work to what you're really good at makes such a big difference.

 

 And maybe a last one is coming to your point about, can everybody afford it? The good news is also digitalisation, because most of our trainings currently run digital, and we have a digital training platform, 100,000 offerings there, prompting courses which, God knows how many people already use. And we are opening this platform also to third parties. It's not that easy, but, but we are opening it so that we can scale it also to others and multiply it.

 

So therefore, it's a journey, but it's worthwhile going that aspect, because, believe it or not, this train is out of the station. AI will define the way, how we go in the future. So you better adapt, we call it growth mindset. Continue learning and give yourself into it and go all over again. Try something. If you fail, try it again. You will learn it. And that's what I believe, is the future.

 

Tian Wei: Mr Shah earlier asked gently whether he should reveal all the business secrets to the others. Let me also ask you a question about future secrets. What about that two to five years. You know, human-machine collaboration, what is the secret map you have in your mind already?

 

Anish Shah: So let me start first with a look at history, because there's a lot of noise around jobs being lost with AI, but in the manufacturing sector, in automotive in particular, most of the jobs are lost with robotics. If you look at what manufacturing plants were like 30 years’ ago and what they are like today, you see a huge difference in terms of people on the shopfloor versus not. So AI is going to change some things, but really not as much as what robotics has done. So I just want to provide that perspective first.

 

So as we look forward, I think the key, as both the Deputy Prime Minister and Mr Busch have mentioned, is going to be around skilling and upskilling, because we're going to create new opportunities. If you think about it from a customer perspective, if we improve what the customer wants, we're going to create new mechanisms or new ways of doing things, and that's what's going to take jobs.

 

So in our history, we've always had multiple things that have taken jobs away and made life easier, in a sense, for people, there always has been a new set of jobs. Another classic example is at the airport. In most places, we don't have a check in counter that we check in, and that, again, has taken away lots of jobs.

 

Tian Wei: But that cannot really be compared to the auto manufacturing right?

 

Anish Shah: Compared to auto manufacturing 30 years’ ago or now, so both perspectives. So these are the things that we got to keep in mind. So it again, starts with the customer.

 

And the last point I'll mention is, the organisation has to be a purpose-driven organisation. So when you start with purpose, it gives a lot more comfort. Again, as Mr Busch mentioned, that comfort is essential for people, that we're going to do things the right way, and if you're going to do things the right way, then you have everyone collaborating with you to make it happen. And to me, that is the most important point for the future.

 

Tian Wei: Ms. Hart, I know she has a great point on that too.

 

Stephanie Pullings Hart: Yeah, I was gonna, complement and maybe just give a slightly different perspective as well, to leveraging AI and what it looks like two to five years from now.

 

You know, quite frankly, I think that we are experiencing in many parts of the world, labour shortages. My kids are not the ones that are raising their hand saying, I'm going to go into manufacturing, you know, or work in a distribution centre. And so this is also an opportunity, I'd like to bring sexy back to manufacturing and distribution and make it attractive. And one of the ways to be able to do that is to really leverage and build on technology and show the opportunities that can exist, so that we can consistently provide you with the best cup of coffee,  every time you drink Nespresso or Nescafe, so that, when we are experiencing different types of downtime activities, we have machines that are learning because they're taking data at milliseconds, and instead of having someone trying to translate all of this information, we have the ability to have it interpreted and provide us with better, long-term, sustainable solutions so that we can continue our path of growth.

 

And so, I think that it complements, as you actually said, AI and humans complement each other, and it's just a different skillset and part of the evolution. And when you think about, maybe for all of us on stage, we remember where the world was when there wasn't a remote control for a television, or there wasn't this thing we call an iPod. There was a record player. There was a cassette player, and you push “play” and record and you know, we've seen this evolution go really fast, but we've all adapted.

 

Tian Wei: Yeah. Thank you very much. I think we open the floor. Your name, where from, your question. Thank you.

 

Audience 1: So I have two questions. One is, I didn’t hear about safety in this whole smart factory thing, because quite a few people become disabled, especially in the suppliers. The second question is, I won't consider these smart factories if they're not built with disability inclusion.

 

Audience 2: My question begins with smart factory. Besides workforce, we have to attract smart people. How do you attract smart people? My students just graduate, get $200,000 from a semiconductor company. How can your company get $200,000 salary?

 

Audience 3: I love the topic of smart factories, but honestly it's around, like, 20 years or even more, the tools have become more powerful. And with AI, of course, you have a booster. Right now, we all talk about competitiveness and growth. And it feels like, smart factories, could be the solution or the accelerator to get there, because you're improving your cost base, improving quality, but, how can we unleash right now these productivity gains and really accelerate smart factories across Europe, across the world, to make a big jump that we all dream of?

 

Audience 4: I wanted to ask about legacy factories versus greenfield factories. Because what we see in the US right now is a lot of discussion about new factories, new investments, based on some of the incentives, etc. And at the same time, the US is lagging behind in a lot of different metrics on adoption. And is that a problem with just a legacy system in general, or is that something unique to the country? Or should we just be pushing for new factories, which is probably unrealistic.

 

Audience 5: How would you turn an old factory into a smart factory?

 

Audience 6: We just heard of the successes, what are the failures we should be looking for?

 

Tian Wei: We got a lot of questions about whether it is new or not, how to update, right? It is related to legacy, related to some of the other things. And the other thing is how to make it more society-friendly. And how to really attract the talent into the areas that you're talking about?

 

Roland Busch: A lot of proof of concepts and scaling technologies is the name of the game, and brownfields is super important. So here comes my point. Number one is what you need to do, is you need to create technology, which is easy to use, plug and play. This is so important because if you want to scale it, if you want to accelerate also in brownfields, don't forget the majority is small and medium-sized enterprises, semi or non-automated, and how to get there? So number one is, it's two ways, or basically three ways. Number one is that we on our turf, make it easy to use, plug and play. So that you go to a brownfield, you plug in an IoT device that just works, and you get your data uploaded and can start running an app from the very beginning.

 

Number two is use ecosystem systems integrators. They're the big guys, Accenture we are working with, but also small ones, which are local, if they train their muscles on this technology, they can help locally. So this is, this is definitely level number two. And the last point is, start at a certain point, which gives you return. I mean, don't make an industrial metaverse on a small manufacturing side to the full version. Start where you have the incremental benefit, and then you can roll it up, one by one, cell by cell.

 

Stephanie Pullings Hart: I’m happy to engage with you after anybody else. I mean, we have 340 manufacturing facilities, and they're not all brand new. And we're not going to continue to build just brand-new factories. And so it is about making the right choices and focus so that you can actually address the opportunity appropriately.

 

DPM Gan: I think this is a very exciting journey that we are embarking on, the transformation to automated, smart factories. To answer your question, the real truth is that we need to create smart jobs, so that the smart people can take up these smart jobs and feel excited about it. And your point that you want to bring sexiness into the manufacturing, you know?  

 

So I think, therefore, I think that there are three key rules that we've generated because of this transformation. The first one is smart operators that operate smart systems. Second rule is a smart integrator to be able to integrate like digital twins. How do I integrate it into the manufacturing facility? You need smart integrators. The last one is smart innovators. So you need to really innovate new systems. And the journey will never end. You will continue to have new innovation, smarter and smarter factories. It's not just smart factories, but smarter factories. So the key is generate smart jobs, so that you can attract smart talent, into this manufacturing sector.

 

Padraig McDonnell: You know, we talked great, fantastic question about scalability of smart factories, and it's about productivity. But what does productivity do? It allows us to invest back into the business. And I fundamentally believe that innovation is at the core of smart factories. So how do we bring it closer to customers? How do we bring more innovation out of factories that are linked to R&D groups? And I think when we are attracting talent, I really believe in the purpose of a company, can really attract talent into industries. And also in these smart factories using these technologies, it's a wonderful jump off point for your career into different aspects of the company.

 

Tian Wei: The value is really behind all of this. Mr Shah and Ms Hart, anything to add?

 

Anish Shah: I'll just quickly add that there are enough technology tools out there, and especially with data being the heart of AI, you don't always need to change machines to be able to create a smart factory.

 

Stephanie Pullings Hart: And maybe the last thing is just, you know, I think one of the essential aspects of doing what all of us are talking about is ensuring that you have leaders that have a clear vision, so that people know what is possible and where they can actually go, and know that they have the support to be able to make those transformations.

 

Padraig McDonnell: One last point, if it's okay, was a key question. Both of our lighthouse factories were in existence for 30 years, or were changed intolLighthouse factories by having a clear vision and not doing small silo changes, but looking at overall changes with end points of why we were actually doing it.

 

Tian Wei: You know, everyone on this panel, well worth five hours of interview, but we try to squeeze them into 45 minutes, and they did such a great job with the help of all of you. So thank you so much, ladies and gentlemen.